Shengpu Tang

423 total citations
10 papers, 230 citations indexed

About

Shengpu Tang is a scholar working on Epidemiology, Health Information Management and Oncology. According to data from OpenAlex, Shengpu Tang has authored 10 papers receiving a total of 230 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Epidemiology, 2 papers in Health Information Management and 2 papers in Oncology. Recurrent topics in Shengpu Tang's work include Sepsis Diagnosis and Treatment (3 papers), Artificial Intelligence in Healthcare (2 papers) and Machine Learning in Healthcare (2 papers). Shengpu Tang is often cited by papers focused on Sepsis Diagnosis and Treatment (3 papers), Artificial Intelligence in Healthcare (2 papers) and Machine Learning in Healthcare (2 papers). Shengpu Tang collaborates with scholars based in United States, Sweden and Australia. Shengpu Tang's co-authors include Jenna Wiens, Michael W. Sjoding, Danai Koutra, Brahmajee K. Nallamothu, Erkin Ötleş, Benjamin Y. Li, John P. Donnelly, Karandeep Singh, John Z. Ayanian and Thomas S. Valley and has published in prestigious journals such as BMJ, Health Affairs and The American Journal of Surgery.

In The Last Decade

Shengpu Tang

10 papers receiving 226 citations

Peers

Shengpu Tang

AI

EPIDE

HI

PHEOH

RNMI

Chung‐Il Wi United States

Miguel Ángel Armengol de la Hoz Spain

Anna Siefkas United States

Hoyt Burdick United States

Catherine Aftandilian United States

Mary Saltz United States

Nancy Gentry United States

Lindsay M. Wong United States

Shu-Xia Li United States

Anna Ostropolets United States

Chung‐Il Wi United States

Shengpu Tang

61 ×0.7

AI

67 ×1.1

EPIDE

52 ×1.2

HI

32 ×0.8

PHEOH

21 ×0.6

RNMI

Citations per year, relative to Shengpu Tang Shengpu Tang (= 1×) peers Chung‐Il Wi

Countries citing papers authored by Shengpu Tang

Since Specialization

Citations

This map shows the geographic impact of Shengpu Tang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shengpu Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shengpu Tang more than expected).

Fields of papers citing papers by Shengpu Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shengpu Tang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shengpu Tang. The network helps show where Shengpu Tang may publish in the future.

Co-authorship network of co-authors of Shengpu Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Shengpu Tang. A scholar is included among the top collaborators of Shengpu Tang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shengpu Tang. Shengpu Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Tang, Shengpu, Stephanie A. Shepard, Ruby-Jean Clark, et al.. (2025). Guiding Clostridioides difficile Infection Prevention Efforts in a Hospital Setting With AI. JAMA Network Open. 8(6). e2515213–e2515213. 2 indexed citations

2.

Sandford, Erin, Rajnish Kumar, Shengpu Tang, et al.. (2024). Analysis of Complete Blood Count Data and Use in Classification Machine Learning Models to Predict Icans Incidence Following CAR-T Cell Therapy. Transplantation and Cellular Therapy. 30(2). S133–S134. 1 indexed citations

3.

Tang, Shengpu, Erkin Ötleş, Dustin McEvoy, et al.. (2022). Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ. 376. e068576–e068576. 38 indexed citations

4.

Tang, Shengpu, Vidhya Gunaseelan, Joceline V. Vu, et al.. (2021). Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients. The American Journal of Surgery. 222(3). 659–665. 13 indexed citations

5.

Spector‐Bagdady, Kayte, Shengpu Tang, W. Nicholson Price, et al.. (2021). Respecting Autonomy And Enabling Diversity: The Effect Of Eligibility And Enrollment On Research Data Demographics. Health Affairs. 40(12). 1892–1899. 17 indexed citations

6.

Tang, Shengpu, et al.. (2020). Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records. JCO Clinical Cancer Informatics. 4(4). 128–135. 28 indexed citations

7.

Singh, Karandeep, Thomas S. Valley, Shengpu Tang, et al.. (2020). Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19. Annals of the American Thoracic Society. 18(7). 1129–1137. 74 indexed citations

8.

Tang, Shengpu, et al.. (2020). Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. arXiv (Cornell University). 1. 9387–9396. 3 indexed citations

9.

Tang, Shengpu, et al.. (2020). Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. Journal of the American Medical Informatics Association. 27(12). 1921–1934. 53 indexed citations

10.

Wang, Jiaxuan, et al.. (2019). Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series.. 27–52. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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